The present invention relates generally to the predicted mechanical properties of cast components and, more particularly to systems, methods, and articles of manufacture to help predict tensile properties and fatigue lives of cast aluminum alloys by determining the distribution of material properties throughout cast components based on casting process simulation that accounts for one or both of dendrite arm spacing (DAS) values and porosity values.
Numerical simulation (such as finite element analysis (FEA, sometimes abbreviated FE) and finite difference (FD)) techniques are used to predict thermal, mechanical and related behavior of an object to be simulated by breaking up large, often complex, objects into discrete simple shapes that are assumed to possess mathematically homogeneous properties. Certain properties, for example, the material properties, are conventionally assumed to be substantially uniform through the object being simulated. Unfortunately, many such objects do not exhibit such uniformity in their material properties. This is particularly prevalent with cast components, where (for example) DAS has been shown to have a significant impact on such material properties, as the material with smaller DAS tends to have better mechanical properties. With regard to automotive engine blocks, DAS values, which provide indicia of solidification rates of cast components, have a tendency to be comparatively low in thin regions or the regions with chills (such as the block's bulkhead), and relatively high in the thicker regions (such as those adjacent the block's head bolt bosses). As such, durability analysis and life prediction (such as fatigue analysis or simulation; or fatigue life prediction) of cast components can be compromised without correction for such material variations.
Regarding DAS-based tensile and fatigue property modelling, the inherent variation in casting properties results at least in part because directional solidification required to feed solidification shrinkage requires temperature gradients that cause differences in solidification rate and time. Since microstructure is sensitive to solidification rate and time, and properties are sensitive to microstructure, a so-called “good” casting design will always produce a gradient of properties. Conventional solidification analysis software reports mechanical properties for various cast metal alloys only in the as-cast condition, or is used to determine microstructure based on a functional relationship with empirical measurements that in turn is used to predict mechanical properties based on the results of solidification simulation combined with specific geometrical and processing inputs.
Because all cast parts are processed to some extent differently, based in part on metal preparation (including, for example, hydrogen and inclusion content), particular casting process features (such as chill or no chill), post cast cooling, heat treatment, geometry or the like, each casting is led through a development cycle ending in a unique set of properties. Further, predicting properties from fundamental principles is very computationally intensive. In one form of fatigue modeling or calculation software, the material property database lists factors that reduce or increase the nominal fatigue strength based on the local microstructural parameter DAS. The software reads a file containing the DAS value at each node in an FEA mesh, and then computes an adjusted fatigue strength at each node. Such a solution fails to show how fatigue strength is controlled by DAS, instead showing how fatigue strength is controlled by porosity content, which only weakly correlates with DAS.
As such, systems, methods and articles of manufacture to accurately account for material property variations of cast components are lacking. Likewise, finite element and related stress analysis could benefit by providing a more realistic field of mechanical properties at each node in the FEA mesh of a cast component as a way to improve simulation accuracy.